Bengaluru: Fractal Analytics has indicated that underlying demand for enterprise artificial intelligence remains stronger than its reported growth suggests, even as weakness in its technology, media and telecom (TMT) business weighed on quarterly performance.
Quarterly Performance and TMT Impact
Fractal’s technology, media and telecom (TMT) business weighed on overall growth during the quarter, with the company saying revenue growth could have been closer to 27% instead of the reported 17% if not for disruptions in that segment. “If those issues were not there, if it was well executed, then we would have been at 27%,” founder and chief executive Srikanth Velamakanni told TOI. The Mumbai-based AI and analytics company, which listed in February this year, reported a quarterly revenue growth of 17% year-on-year to Rs 886 crore in the period of January to March, while profit rose 109% to Rs 116 crore. For the full fiscal year of 2025-26, revenue increased 19% to Rs 3,300 crore and profit rose 30% to Rs 287 crore.
Shift to Outcome-Based Pricing
The company is increasingly repositioning itself from a traditional analytics services provider toward an enterprise AI transformation platform focused on outcome-based pricing and software-like economics. “The conversation will move from input-based to output-based in this industry in the next three-four years,” Velamakanni said. About 40% of Fractal’s business currently comes from output-, outcome- and license-linked engagements, which the company aims to increase to 60% over the next two to three years. Velamakanni said license revenue carries materially higher margins than traditional services work.
Healthcare and Life Sciences Growth
The company’s healthcare and life sciences business has also emerged as one of its fastest-growing verticals, reflecting broader global adoption of AI across regulated industries. “Healthcare and life sciences are really expanding their AI usage,” he said.
AI Adoption Moving Beyond Experimentation
Velamakanni said the current wave of AI adoption is moving beyond experimentation and toward enterprise-wide workflow redesign, driven by increasingly autonomous AI systems capable of performing longer and more complex tasks. “Now it can run for 10 hours without getting into trouble and get things done,” he said, referring to advances in agentic AI systems. He added that large companies are still in the early stages of redesigning organizational structures around AI, though broader workforce transformation could accelerate over the next two years.
India’s Sovereign AI Ambitions
On India’s sovereign AI ambitions, Velamakanni argued that the country should continue investing in frontier AI capabilities despite rapid advances by global players such as Anthropic and DeepSeek. “The best time to do it was probably three years ago. But the second-best time is always today,” he said.
